In this chapter, we will wrap up some of the concepts behind deep reinforcement learning (deep RL) algorithms that we explained in the previous chapters to give you a broad view of their use and establish a general rule for choosing the most suitable one for a given problem. Moreover, we will propose some guidelines so that you can start the development of your own deep RL algorithm. This guideline shows the steps you need to take from the start of development so that you can easily experiment without losing too much time on debugging. In the same section, we also list the most important hyperparameters to tune and additional normalization processes to take care of.
Then, we'll address the main challenges of this field by addressing issues such as stability, efficiency, and generalization. We'll use these three main...
